Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations

PloS One
David J AlbersGeorge Hripcsak

Abstract

Using glucose time series data from a well measured population drawn from an electronic health record (EHR) repository, the variation in predictability of glucose values quantified by the time-delayed mutual information (TDMI) was explained using a mechanistic endocrine model and manual and automated review of written patient records. The results suggest that predictability of glucose varies with health state where the relationship (e.g., linear or inverse) depends on the source of the acuity. It was found that on a fine scale in parameter variation, the less insulin required to process glucose, a condition that correlates with good health, the more predictable glucose values were. Nevertheless, the most powerful effect on predictability in the EHR subpopulation was the presence or absence of variation in health state, specifically, in- and out-of-control glucose versus in-control glucose. Both of these results are clinically and scientifically relevant because the magnitude of glucose is the most commonly used indicator of health as opposed to glucose dynamics, thus providing for a connection between a mechanistic endocrine model and direct insight to human health via clinically collected data.

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Citations

Mar 1, 2015·Journal of the American Medical Informatics Association : JAMIA·George HripcsakAdler Perotte
Aug 1, 2015·Journal of the American Medical Informatics Association : JAMIA·Jie XuEnid Montague
Apr 28, 2017·PLoS Computational Biology·David J AlbersLena Mamykina
Feb 15, 2017·Journal of Biomedical Informatics·Matthew A LevinEimear E Kenny
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Oct 19, 2017·Journal of the American Medical Informatics Association : JAMIA·George Hripcsak, David J Albers
Dec 15, 2020·Journal of Biomedical Informatics·Elliot G MitchellDavid J Albers

Related Concepts

Glucose
Information Centers
Insulin
Analysis
Electronic Health Records

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